Abstracts of the 4th Biennial Schizophrenia International Research Conference / Schizophrenia Research 153, Supplement 1 (2014) S1–S384 S373

Poster #T235

DEFICIENT CORTICAL ACTIVITY DURING MOTOR INHIBITION IN

SCHIZOPHRENIA

Maxime Térémetz1,2, Caroline Malherbe2, Marie-Odile Krebs3,

Catherine Oppenheim4, Pavel Lindberg2, Isabelle Amado5

1CESEM; 2Université Paris Descartes; 3Sainte Anne hospital, Paris; 4Centre

hospitalier Sainte-Anne, Service d’Imagerie Morphologique et Fonctionnelle,

Paris, France; 5INSERM U894, Laboratory “Pathophysiology of Psychiatric

Disorders”, GDR3557, Centre of Psychiatry and Neurosciences, Paris, France

Background: Deficient inhibition is one key mechanism in schizophrenia

as evidenced by numerous behavioural and imaging studies. Functional

MRI (fMRI) has shown altered activation patterns during inhibition tasks

in patients with schizophrenia (e.g., Kaladjian et al, 2007; Criaud and

Boulinguez, 2013). However, findings across studies are not consistent.

The aim of this study was to investigate cortical activation during motor

inhibition in patients with schizophrenia.

Methods: 19 stabilized patients with schizophrenia (mean age: 32, 18

males) treated with atypical antipsychotic medication were compared

with 19 siblings (mean age: 32, 8 males) and 23 healthy subjects (mean

age: 30, 12 males). Subjects underwent a single 3T-MRI session including

event-related fMRI (EPI BOLD sequence, 4 runs). Participants performed

a volitional inhibition task consisting in a fingertip Go-NoGo task, with

30% randomized NoGo events. Preprocessing and analysis was performed

using spm5 (www.fil.ion.ucl.ac.uk/spm/). Two analyses were performed: (i)

whole brain statistical maps of Prep (preparation), NoGo>baseline (acti-

vation) and baseline>NoGo (deactivation) with age, gender and years of

study as covariates; (ii) extraction of NoGo contrast values using Marsbar

toolbox in 12 cortical regions of interest (ROIs) involved in NoGo inhibition

(Criaud and Boulinguez, 2013). Results were compared between the three

groups.

Results: The three groups suppressed finger movements similarly during

NoGo trials. For whole brain analysis and ROI analysis, controls activated

SMA and M1 during preparation and deactivated these areas during mo-

tor inhibition. Both activation during preparation and deactivation during

inhibition were reduced in patients and siblings compared to controls. No

difference was found between patients and siblings.

Discussion: The deficient brain processing during successful motor in-

hibition suggests that patients with schizophrenia may use alternative

inhibition strategies to achieve similar performance levels to controls.

The similarity between schizophrenia patients and siblings suggests that

the altered brain activity patterns found during motor inhibition could

reflect a genetically predetermined vulnerability trait linked to the neuro-

developmental compound of schizophrenia.

Poster #T236

ENVIRONMENTAL INFLUENCES ON SYMPTOMATOLOGY OF A FIRST

EPISODE PSYCHOSIS

Manuel Tettamanti1, Paolo Ghisletta2, Adriano Zanello3,

Panteleimon Giannakopoulos3, Marco C.G. Merlo4, Logos Curtis3

1Hopitaux Universitaires de Geneve; 2University of Geneva; 3Geneva

University Hospitals; 4University Hospitals of Fribourg

Background: Some studies have shown an effect of environment on symp-

tomatology of adults’ diagnosed with Schizophrenia (Jiang et al., 2013;

Ruggeri et al., 2005). For example, patterns of correlations between symp-

toms’ dimensions could vary between different social contexts. However,

such literature is very spares in young adults’ populations with first episode

psychosis (FEP). The main objective of this study is to investigate whether

there are differences in underlying symptoms’ dimensions in a population

of FEP across different environments and trajectories of care.

Methods: Brief Psychiatric Rating Scale (BPPRS 24; Ventura & al., 1993)

data were collected from 243 young adults (17-31 years old) within the

JADE unit (i.e. specialized unit for early recognition and treatment of mental

disorders) at their entry in two different care structures (i.e. Outpatients

(n= 79) or Inpatients (n=164)) after a FEP. Measures of Global functioning

(Cornblatt et al., 2007) and sociodemographical data were also obtained.

Exploratory (with SPSS 19) and confirmatory factor analyses (with AMOS

19) of the BPRS 24 were computed.

Results: A five-factor solution was obtained (i.e. manic/excitement, negative

symptoms, depression/anxiety, positive symptoms and disorganisation), ex-

plaining almost 60% of the variance for both Outpatients and Inpatients.

However, results of confirmatory analyses indicate a poor fit of our model

(χ2 (df=220, N=299) = 737.844, p<0.001, CFI = 0.83; RMSEA = 0.09)

when applied on the whole sample. Further analyses showed that this

could be explained by notable differences in factorial structure of BPRS

across the two groups. More specifically, BPRS 24 results between care

structures differed in the factor that explained the greatest proportion of

variance: Manic/excitement for Inpatients (i.e. 24% vs. 9% for Outpatients)

and negative dimension for Outpatients (i.e. 31% vs. 17% for Inpatients).

Moreover, important differences emerged in latent correlations between

structures of care. We found especially significant negative correlations

between manic/excitement and depression/anxiety for in Outpatient struc-

ture (−0.26; p<0.001) but a positive correlation in the Inpatient one (0.59;

p<0.001). Finally, symptom severity (i.e. mean score) for Inpatients was

significantly higher than for Outpatients for all dimensions (p<0.001),

excepted for negative symptoms.

Discussion: Notable differences in symptoms’ dimensions and patterns of

correlations appear across the two care structures. Our results suggest that,

despite BPRS 24 being especially suited for evaluating symptom dimensions

for FEP, clinicians have to be attentive to environmental influences on

expression of symptomatology (see also Ruggeri et al., 2005). We suggest a

symptom-oriented approach (cf. Bentall, 2006) to be more useful compared

to a broad diagnostic classification.

Poster #T237

USING STRUCTURAL NEUROIMAGING TO MAKE QUANTITATIVE

PREDICTIONS OF SYMPTOM PROGRESSION IN INDIVIDUALS AT

ULTRA-HIGH RISK FOR PSYCHOSIS

Stefania Tognin1, William Petterson-Yeo2, Isabel Valli3, Chloe Hutton4,

James Woolley5, Paul Allen1, Philip McGuire6, Andrea Mechelli1

1Institute of Psychiatry, Kings College London; 2Institute of Psychiatry,

Department of Psychosis Studies, King’s College London; 3Institute of

Psychiatry; 4Wellcome Trust Centre for Neuroimaging, UCL Institute of

Neurology, University College London; 5Division of Experimental Medicine,

Imperial College London, London, UK; 6King’s College London

Background: Neuroimaging holds the promise that it may one day aid

the clinical assessment of individual psychiatric patients. However, the

vast majority of studies published so far have been based on average

differences between groups, which do not permit accurate inferences at the

level of the individual. We examined the potential of structural Magnetic

Resonance Imaging (MRI) data for making accurate quantitative predictions

about symptoms progression in individuals at ultra-high risk for developing

psychosis.

Methods: Forty people at ultra-high risk for psychosis were scanned using

structural MRI at first clinical presentation and assessed over a period

of two years using the Positive and Negative Syndrome Scale (PANSS).

Using a multivariate machine learning method known as relevance vector

regression (RVR), we examined the relationship between brain structure at

first clinical presentation, characterized in terms of gray matter volume and

cortical thickness, and symptom progression at 2 year follow-up.

Results: The application of RVR to whole-brain cortical thickness MRI data

allowed quantitative prediction of clinical scores with statistically signifi-

cant accuracy (correlation = 0.34, p=0.026; Mean Squared-Error = 249.63,

p=0.024). This prediction was informed by regions traditionally associated

with schizophrenia, namely the right lateral and medial temporal cortex

and the left insular cortex. In contrast, the application of RVR to gray matter

volume did not allow prediction of symptom progression with statistically

significant accuracy.

Discussion: These results provide proof-of-concept that it could be pos-

sible to use structural MRI to inform quantitative prediction of symptom

progression in individuals at ultra-high risk of developing psychosis. This

would enable clinicians to target those individuals at greatest need of pre-

ventative interventions thereby resulting in a more efficient use of health

care resources.